Saturday, August 2, 2008

The essence of designing research to permit causal inference is that the investigator can arrange things so that a single element (independent variable) can be isolated that distinguishes the treatment of two groups. Then, when the groups are later compared on an outcome (dependent) variable, a causal inference becomes inescapable because the groups differed in only one way (their respective form of specific treatment) leading up to the outcome.

In experimental studies, techniques including random assignment to conditions of the IV, alternative activities to occupy the control group's time and efforts, and double-blindness, help ensure as best as possible that the treatment of the experimental and control groups differs only on the one essential element that is of scientific interest.

In non-experimental research, isolating the key differentiating factor between the experience of two groups is much more difficult. We may observe groups in society that appear to differ on Factor A (of interest to us), but they may also differ on Factors B, C, D, etc. For example, we may be interested in how our Factor A, type of school attended (public or private), affects the outcome of standardized test scores. Children who differ on Factor A may also, however, tend to differ on family income (Factor B), neighborhood environment (Factor C), etc. Ultimately, therefore, any difference seen in the final outcome measure between children who attended public and private schools cannot be pinpointed definitively to have been caused by school type (Factor A), because Factors B and C also distinguished the groups' experiences.

Writing on the New York Times "Freakonomics" blog, Justin Wolfers reviews a study by Scott Carrell and Mark Hoekstra on whether the presence of a disruptive child in a classroom can adversely affect the learning and behavior of the other children. To obtain an objective measure of children's likely disruptiveness, Carrell and Hoekstra examined official records looking for children who came from a home in which there was an allegation of domestic violence. Here are some key excerpts from Wolfers's entry (bold emphasis added by me):

Around 70 percent of the classes in their sample have at least one kid exposed to domestic violence. The authors compare the outcomes of that kid’s classmates with their counterparts in the same school and the same grade in a previous or subsequent year — when there were no kids exposed to family violence — finding large negative effects.

Adding even more credibility to their estimates, they show that when a kid shares a classroom with a victim of family violence, she or he will tend to under-perform relative to a sibling who attended the same school but whose classroom had fewer kids exposed to violence. These comparisons underline the fact that the authors are isolating the causal effects of being in a classroom with a potentially disruptive kid, and not some broader socio-economic pattern linking test scores and the amount of family violence in the community.

In a research area such as this, where random-assignment experiments are impossible to conduct, Carrell and Hoekstra have thus done their best to hold everything constant -- the school, the grade level, and, via the sibling component, children's home environment -- so that observed differences in children's school performance can more confidently be attributed to the putative effect of having a disruptive classmate.

Sibling designs are used fairly often in social science research. We hope to provide more in-depth discussion of this approach in our future postings.